Generally, many vision based applications require finding matching points of interest across digital images of a scene or an object captured at different camera positions and/or orientations. The points of interest can be points, sets of points, lines, segments, edges, corners, blobs or regions. The points of interest can also be a group of pixels. For example, in panorama, image registration requires points of interest to be matched across digital images.
Existing approaches extract the points of interest having high repeatability from the digital images. That is, the probability that same sets of points of interest extracted from different digital images is high. Further, the existing approaches form a feature descriptor substantially around each point of interest, based on its neighborhood pixels, to enable comparison and matching of the points of interest. Typically, a feature descriptor requires ensuring that same point of interest in different digital images is described in a similar way with respect to a similarity measure, which may include distinctiveness, i.e., different points of interest result in different feature descriptors and also require invariance to changes in viewing direction, rotation, changes in illumination and/or image noise.
However, the existing approaches assume that all digital images are captured in an upright camera orientation and therefore, may not address the problems associated with changes in orientation. Consequently, this may result in higher probability of mismatches. Further, using the feature descriptor determined by the existing approaches may lead to higher mismatches when digital images contain multiple congruent or near-congruent points of interest, for example, four corners of a symmetric window or individual dartboard sections.
The systems and methods disclosed herein may be implemented in any means for achieving various aspects. Other features will be apparent from the accompanying drawings and from the detailed description that follow.